| Part | Chapter | Topic |
|---|---|---|
| Part 1 Strategic Context | Chapter 1 | Introduction & Value Proposition |
| Chapter 2 | The Traditional CCA Process (Before Florence) | |
| Chapter 3 | The CCA Lifecycle with Florence (5 Phases) | |
| Part 2 Pre-Encounter | Chapter 4 | The Navigator's View — Automating Pre-Encounter Tasks |
| Chapter 5 | The Engage Workflow — AI-Powered Patient Outreach | |
| Part 3 Encounter | Chapter 6 | The Provider's View — A Seamless Encounter |
| Chapter 7 | Provider Best Practices & Objection Handling | |
| Part 4 Post-Encounter | Chapter 8 | The Convene Workflow — Care Team Collaboration |
| Chapter 9 | The Check-In Workflow — Proactive Patient Monitoring | |
| Chapter 10 | Filing to the EHR & Conclusion | |
| Appendices | A & B | Screenshot Reference & Demo Cheat Sheet |
Audience: This manual is designed for sales representatives at NightingaleMD. It provides the strategic context, workflow knowledge, and demo scripts needed to effectively showcase the Florence AI Navigator to prospective clients, including clinical stakeholders (CMOs, quality directors) and practice managers.
Purpose: The primary purpose of this manual is to serve as a comprehensive guide for conducting live client demos. It will equip you with the knowledge to not only demonstrate the features of Florence but also to articulate its profound value proposition within the complex ecosystem of Comprehensive Care Assessment (CCA).
*These metrics are based on NightingaleMD's internal pilot program conducted in Q3 2025 with a sample of 12 participating practices. Results may vary based on practice size, patient population, and implementation approach. Full methodology available upon request.
| Metric | Impact | Description |
|---|---|---|
| Navigator Workload Reduction | 70%* | Automates manual tasks like chart review, patient outreach, and documentation preparation, freeing navigators to focus on high-value patient interactions. |
| Gap Closure Rate Improvement | 40%* | Proactively identifies and stages all care gaps from COOP, ensuring providers have the information they need at the point of care. |
| Automated Documentation | 100%* | Generates compliant, MEAT-criteria documentation in real-time as providers accept gaps, eliminating the documentation burden. |
| Enhanced Patient Engagement | Significant | Utilizes AI-powered voice and SMS to conduct TCM outreach, schedule appointments, and monitor patients. |
The traditional Comprehensive Care Assessment (CCA) process is a manual, time-consuming, and often inefficient workflow that places a heavy burden on care navigators and providers. Understanding these pain points is critical to articulating the value of Florence.
| Pain Point | Description |
|---|---|
| Time-Consuming | The manual process can take 4–6 hours of navigator time per patient, limiting the number of patients a single navigator can manage. |
| Inconsistent Gap Identification | Manual chart review is prone to human error, leading to missed care gaps and lost revenue opportunities. |
| Provider Documentation Burden | Providers spend significant time on documentation, taking away from patient care. |
| Missed Follow-up | Manual follow-up is often inconsistent, leading to poor care coordination and patient outcomes. |
| Limited Scalability | The manual process is not scalable, making it difficult for practices to manage a growing patient population. |
Florence transforms the CCA process by automating and enhancing each phase of the lifecycle, from trigger to ongoing management. This integrated approach ensures a seamless, efficient, and effective workflow across all five phases.
Florence automatically detects CCA triggers from COOP in real-time, including hospital discharge notifications, Annual Wellness Visit (AWV) due dates, new quality measure gaps, and suspect diagnosis gaps. When a trigger is detected, Florence immediately begins compiling the patient's clinical profile and initiating the pre-encounter workflow. In the demo, Jane Doe's discharge from St. Joseph's Hospital triggered Florence to begin the entire CCA process automatically.
Once a trigger is detected, Florence initiates the pre-encounter workflow. This includes compiling all necessary CCA components from COOP and the EHR, automatically scheduling and conducting the TCM outreach call using the Engage workflow, confirming the follow-up appointment with the patient, and staging all identified care gaps for provider review. The Engage workflow uses AI-powered voice to conduct natural, empathetic conversations with patients, handling medication verification, appointment scheduling, and symptom screening.
During the patient visit, Florence acts as a real-time copilot for the provider. The Nightingale Navigator sidebar displays all staged gaps with supporting evidence directly within athenaOne. The provider can accept, reject, or defer each gap with a single click, and Florence auto-generates MEAT-criteria documentation in real-time as gaps are accepted. This eliminates the documentation burden and ensures compliance.
After the visit, Florence automates the post-encounter workflow. This includes coordinating specialist referrals and follow-up appointments, sending automated patient education materials, and using the Convene workflow to facilitate care team meetings. Convene enables three-way calls between the patient, provider, and specialists, with real-time transcription and documentation. The File to EHR function uses generative browsing to securely file all documentation to athenaOne with a single click.
Florence provides ongoing support for patients with chronic conditions using the Check-In workflow. This SMS-based monitoring system conducts routine check-ins, tracks medication adherence and care plan progress, monitors symptoms, and escalates to a human navigator when necessary. For Jane Doe, this means daily post-discharge monitoring to prevent readmission and ensure recovery compliance.
This chapter details the pre-encounter workflow from the care navigator's perspective. The goal is to demonstrate how Florence automates the most time-consuming manual tasks, allowing navigators to operate at the top of their license.
When you first open the Florence AI Navigator, you see the side-by-side view: the athenaOne EHR on the left and the Florence Copilot sidebar on the right. The patient's chart is already loaded with all relevant clinical data, including the HPI, vitals, screenings, problem list, and medications.
The Florence Copilot sidebar immediately shows the patient's key information at a glance. Jane Doe is flagged as High Risk and enrolled in CCM (Chronic Care Management). The three workflow buttons — Engage, Convene, and Check-In — are prominently displayed. Below the buttons, the Florence Summary dropdown provides a quick clinical overview, and the Care & Diagnosis Gaps section shows all 9 identified gaps.
The Care & Diagnosis Gaps section shows all 9 gaps that Florence has automatically identified from COOP. The gaps are organized by category using filter buttons: All (9), Recapture (0), Suspect (4), Quality (3), and Frailty (2). The initial view shows the first two gaps; scrolling reveals additional gaps including Type 2 Diabetes with Hyperglycemia, Colorectal Cancer Screening, Statin Therapy, Nephrology Referral, Fall Risk Assessment, Comprehensive Frailty Assessment, and Morbid Obesity.
Sales Rep: "What you're seeing here is the Florence AI Navigator, integrated directly into athenaOne. The moment Jane Doe was discharged from the hospital, Florence received a notification from COOP and automatically initiated the pre-encounter workflow.
Florence has already compiled all of Jane's relevant information, including her discharge summary, medications, and all of her open care gaps from COOP. You can see here that Florence has identified 9 gaps in total: 4 suspect gaps, 3 quality measures, and 2 frailty indicators. Let me scroll down to show you the full list."
Clicking the Details button on any gap reveals comprehensive information including the full ICD-10 description, supporting evidence from the patient's chart, and the clinical rationale for the gap identification. This level of detail is what enables providers to make informed decisions at the point of care.
Each gap has a "Stage for MD Review" button that the navigator uses to prepare gaps for the provider encounter. The navigator reviews each gap and stages the ones that are relevant for the upcoming visit. Gaps that are not appropriate for the current visit can be deferred using the "Future Visit" button. This targeted staging ensures the provider sees only the most relevant gaps during the encounter.
Sales Rep: "Now, let's look at how Florence prepares for the provider encounter. The navigator reviews each gap and clicks 'Stage for MD Review' to prepare it for Dr. Campbell. Florence has also automatically generated the necessary MEAT criteria documentation based on the information from COOP. This ensures that the provider has everything they need to close the gaps and that the documentation is compliant."
The Engage workflow is Florence's AI-powered voice outreach tool. It automates a wide range of patient communication tasks, from TCM calls to appointment reminders, using natural language processing to have human-like conversations with patients.
| Feature | Description |
|---|---|
| Natural Language Conversation | Engage uses advanced NLP to have human-like conversations with patients, adapting tone and content based on patient responses. |
| Automated Scheduling | Engage can automatically schedule appointments based on the provider's availability and the patient's preferences. |
| Real-time Escalation | If a patient expresses confusion, distress, or reports concerning symptoms, Engage automatically escalates the call to a human navigator. |
| Full Transcription | Every call is transcribed in real-time and saved to the patient's record for documentation and compliance purposes. |
Clicking the Engage button initiates the AI-powered TCM outreach call. The button changes to "In Call..." to indicate the call is in progress, as shown in Figure 7. Florence automatically dials the patient and begins the conversation using the appropriate protocol (in this case, a post-discharge TCM call).
The Transcript tab shows the real-time conversation between Florence and the patient. Florence initiates the call with a warm, professional greeting and confirms the patient's identity before proceeding with the TCM protocol. The full transcript is automatically saved to the patient's record.
Florence: "Hello, I'm calling from Dr. Campbell's office on behalf of Nightingale Health. May I please speak with Jane Doe?"
Jane Doe: "This is she."
Florence: "Hi Jane, my name is Florence, and I'm an AI assistant with Nightingale Health. I'm calling to check in with you after your recent discharge from St. Joseph's Hospital. How are you feeling?"
Jane Doe: "Oh, hello. I'm feeling a little tired, but I'm doing okay."
Florence: "I'm glad to hear that. I'm calling to make sure you have everything you need for a smooth recovery. I see that you were prescribed a few new medications. Have you been able to pick them up from the pharmacy?"
Jane Doe: "Yes, I have them right here."
Florence: "That's great. It's also very important that you have a follow-up appointment with your primary care provider, Dr. Campbell, within the next week. I see one has been scheduled for you on Monday, February 18th at 10:00 AM. Does that time still work for you?"
Jane Doe: "Yes, that's perfect."
Florence: "Wonderful. We look forward to seeing you then. If you have any questions before your appointment, please don't hesitate to call our office. Have a great day, Jane."
Jane Doe: "Thank you, you too."
After the Engage call completes, the Documentation tab shows the auto-generated clinical documentation. Florence automatically creates a structured summary of the call, including key findings, patient responses, and next steps — all formatted to meet MEAT criteria compliance requirements.
Sales Rep: "As you can see, Florence had a natural, empathetic conversation with Jane, confirmed her medications, and scheduled her follow-up appointment with Dr. Campbell. This entire process was fully automated, saving the care navigator significant time. The transcript and documentation are automatically saved to Jane's record for compliance purposes."
This chapter focuses on the provider's experience during the patient encounter. The key is to demonstrate how Florence acts as an intelligent copilot, streamlining the provider's workflow and enabling them to focus on patient care, not documentation.
The Provider View displays all staged gaps with expanded clinical information, including the full ICD-10 description and supporting evidence from the chart. The provider can review each gap, accept or reject it, and Florence automatically generates the MEAT-criteria documentation in real-time.
Sales Rep: "We're now looking at Dr. Campbell's view in athenaOne. The Florence AI Navigator is seamlessly integrated into the EHR, providing real-time decision support right at the point of care. All the work the navigator did in the pre-encounter phase is now available to Dr. Campbell.
The Florence sidebar on the right displays all 9 of Jane's care gaps that were staged by the navigator. The gaps are organized by type — Suspect, Quality, and Frailty — making it easy for Dr. Campbell to review them. Let's say Dr. Campbell wants to address the Chronic Kidney Disease Stage 3a suspect gap. He can simply click on it to see the supporting evidence from COOP."
The left panel of the dashboard shows the full athenaOne EHR patient chart, including the Subjective section (HPI, ROS), Objective section (Vitals, Screenings & Findings), Problem List, Medications, Referrals, and more. Florence integrates seamlessly alongside this existing workflow without disrupting the provider's familiar EHR experience.
| Clinical Data Point | Value | Significance |
|---|---|---|
| Chief Complaint | Post-hospital discharge follow-up (TCM call) | Triggers TCM billing requirements |
| Blood Pressure | 142/88 mmHg | Elevated; supports hypertension gap |
| BMI | 29.3 | Overweight; relevant to obesity gap |
| PHQ-9 Score | 8 (Mild) | Supports depression screening but may not support moderate severity coding |
| Morse Fall Scale | 55 (High Risk) | Supports frailty assessment gap |
| Last A1c | 8.9% (12/20/2025) | Elevated; supports diabetes management gap |
| eGFR | 42 mL/min | Stage 3a CKD; supports nephrology referral |
Trust the Staged Gaps: The care gaps staged by Florence are based on real-time data from COOP, which is the source of truth for gap validation. Providers can trust that these gaps are accurate and up-to-date.
Leverage the Sidebar: The Florence sidebar is designed to be an at-a-glance resource. Providers should use it to quickly review all open gaps and supporting evidence without leaving the patient's chart.
Embrace One-Click Attestation: The one-click MEAT criteria attestation is a powerful time-saving feature. Providers should use it to quickly document their clinical decisions rather than manually typing notes.
Review Auto-Generated Documentation: While Florence's documentation is highly accurate, providers should always give it a quick review before signing the note. This ensures clinical accuracy and maintains the provider's professional responsibility.
The Convene workflow facilitates seamless communication and collaboration among the patient's care team. It allows navigators to quickly schedule and launch three-way calls between the patient, the provider, and any other relevant stakeholders (e.g., specialists, family members).
| Feature | Description |
|---|---|
| Three-Way Calling | Easily initiate three-way calls with the patient and other care team members for coordinated care discussions. |
| Automated Scheduling | Schedule calls in advance and send automated reminders to all participants. |
| Real-time Transcription | All calls are transcribed in real-time, and the transcript is automatically saved to the patient's chart. |
Clicking the Convene button initiates a three-way call. The button changes to "In Session..." to indicate the call is in progress, as shown in Figure 11. Florence automatically dials the patient first, then connects the care team member.
Sales Rep: "Let's imagine that during Jane's visit, Dr. Campbell decides it would be beneficial to have a follow-up conversation with her cardiologist. With Florence, he doesn't have to waste time playing phone tag. The navigator can simply use the Convene workflow to schedule a three-way call. Florence will automatically dial all participants and connect them. The entire conversation is transcribed in real-time and saved to Jane's chart."
The Check-In workflow is Florence's automated patient monitoring tool. It uses SMS to proactively check in with patients, track their progress, and identify potential issues before they become serious.
| Feature | Description |
|---|---|
| Automated SMS Outreach | Send automated, personalized SMS messages to patients to check on their health status and medication adherence. |
| Customizable Protocols | Create custom check-in protocols for different chronic conditions (e.g., diabetes, hypertension, CHF, post-discharge). |
| Real-time Alerts | If a patient reports a concerning symptom, Florence automatically alerts the care navigator for immediate follow-up. |
Clicking the Check-In button initiates the SMS-based patient monitoring workflow. Florence sends personalized messages based on the patient's condition and care plan, and monitors responses for any concerning symptoms that may require escalation.
Sales Rep: "Florence's support for Jane doesn't end when she leaves the office. Using the Check-In workflow, the care navigator can set up a protocol to monitor Jane's recovery. Florence sends Jane daily SMS messages to ask about her symptoms and medication adherence. If Jane reports any issues — like shortness of breath or dizziness — Florence immediately alerts the care navigator. This proactive approach helps prevent hospital readmissions and improve patient outcomes."
Once the provider has reviewed and accepted the care gaps, Florence makes it easy to file all necessary documentation to the EHR. This is a critical step in closing the loop and ensuring the patient's chart is complete and up-to-date.
| Feature | Description |
|---|---|
| Generative Browsing | Florence uses generative browsing (not an API) to securely and reliably file documentation to athenaOne, working within the existing EHR interface. |
| One-Click Filing | With a single click of the "File to EHR" button, all auto-generated documentation is filed to the patient's chart. |
| Real-time Sync | The sync between Florence and the EHR is real-time, ensuring the patient's chart is always up-to-date. |
Florence is more than just a tool; it is a new way of thinking about Comprehensive Care Assessment. By automating the manual, time-consuming tasks that have traditionally burdened care navigators and providers, Florence empowers them to focus on what they do best: providing high-quality, patient-centered care.
*Based on NightingaleMD internal pilot program, Q3 2025. Results may vary. Full methodology available upon request.
| Figure | Description | Key Elements Shown |
|---|---|---|
| 1 | Full Dashboard Overview | athenaOne EHR + Nightingale Navigator sidebar, patient chart, care gaps |
| 2 | Copilot Sidebar (Zoomed) | Patient summary, workflow buttons, Florence Summary, gap filters |
| 3 | Care & Diagnosis Gaps (Initial) | First 2 of 9 gaps: CKD Stage 3a, Major Depressive Disorder |
| 4 | Care & Diagnosis Gaps (Scrolled) | Additional gaps: Type 2 Diabetes, Colorectal Cancer Screening, Statin Therapy |
| 5 | Gap Details Expanded | Full ICD-10 description, supporting evidence, clinical rationale |
| 6 | Stage for MD Review Buttons | Staging buttons, "Show staged items" filter, gap categories |
| 7 | Engage "In Call..." State | Engage button showing active call status |
| 8 | Engage Transcript | Real-time conversation with expanded gap details |
| 9 | Engage Documentation | Auto-generated clinical documentation from the call |
| 10 | Provider View (Full Dashboard) | Provider perspective with staged gaps and clinical data |
| 11 | Convene "In Session..." State | Convene button showing active session status |
| 12 | Check-In SMS Transcript | SMS-based patient monitoring conversation |
For a streamlined 15–20 minute demo, follow this flow:
| Step | Action | Key Talking Point | Time |
|---|---|---|---|
| 1 | Open Dashboard | Show side-by-side EHR + Copilot layout | 1 min |
| 2 | Review Patient Summary | Highlight High Risk, CCM badges, workflow buttons | 1 min |
| 3 | Review Gaps | Show 9 auto-identified gaps from COOP, scroll through list | 2 min |
| 4 | Expand a Gap | Show CKD Stage 3a details with ICD-10 and evidence | 2 min |
| 5 | Stage Gaps | Demonstrate staging for MD review | 1 min |
| 6 | Click Engage | Show "In Call..." state, demonstrate AI-powered TCM call | 3 min |
| 7 | Show Transcript | Walk through the natural conversation | 2 min |
| 8 | Show Documentation | Highlight auto-generated clinical notes | 1 min |
| 9 | Provider View | Show provider's perspective with MEAT criteria | 2 min |
| 10 | Click Convene | Demonstrate three-way calling | 2 min |
| 11 | Click Check-In | Demonstrate SMS monitoring | 1 min |
| 12 | Wrap Up | Summarize KPIs, answer questions | 2 min |
Total Demo Time: 15–20 minutes
Application URL: https://florence-intel-dashboard.vercel.app